We find that HaloNets perform at par or slightly better (Figure REF ) than EfficientNet models for the same parameters, outperforming other model families. Our best model, H7, achieves 84.9% top-1 ImageNet validation accuracy and 74.7% top-1 accuracy on ImageNet V2 [1]} (with a -0.5% gap to the linear fit in [1]}). For each of our HaloNet models, we use image sizes comparable to the corresponding EfficientNet model, training on images sizes up to \(600\times 600\) . (Table REF ).
For a comparison of our latencies with EfficientNet, the reader can refer to Section .
To the best of our knowledge, these results are the first to show that self-attention based models for vision perform on par with the SOTA for image classification when trained on imagenet from scratch. Note that for all our experiments, we report accuracies at the end of training and we tune regularization hyperparameters such as augmentation hyperparameters for the baselines and HaloNet models.
